我能够得到它的工作。我在这里粘贴了一些代码,可能有助于某些代码。不是很优雅 - 但工作。
def shuffle_in_unison(a, b): #courtsey http://stackoverflow.com/users/190280/josh-bleecher-snyder assert len(a) == len(b) shuffled_a = np.empty(a.shape, dtype=a.dtype) shuffled_b = np.empty(b.shape, dtype=b.dtype) permutation = np.random.permutation(len(a)) for old_index, new_index in enumerate(permutation): shuffled_a[new_index] = a[old_index] shuffled_b[new_index] = b[old_index] return shuffled_a, shuffled_b
def createDataSet(imagefolder):
os.chdir(imagefolder)
# total number of files
number_of_files = len([item for item in os.listdir('.') if os.path.isfile(os.path.join('.', item))])
# get a shuffled list : I needed this because my image names were of the format n_x_<some details>.jpg
# where n was my target and x was a number from 0 to m-1 where m was the number of samples
# of the target value n. So I needed so shuffle and iterate while putting images in train
# test and validate arrays
image_index_array = range(0,number_of_files)
random.seed(12)
random.shuffle(image_index_array)
# split 80/10/10 - train/test/val
trainsize = int(number_of_files*.8)
testsize = int(number_of_files*.1)
valsize = number_of_files - trainsize - testsize
# create the random value arrays of train/test/val by slicing the total image index array
train_index_array = image_index_array[0:trainsize]
test_index_array = image_index_array[trainsize:trainsize+testsize]
validate_index_array = image_index_array[trainsize+testsize:]
# initialize the data structures
dataset = {'train':[[],[]],'test':[[],[]],'validate':[[],[]]}
i_counter = 0
train_X = []
train_y = []
test_X = []
test_y = []
val_X = []
val_y = []
for item in os.listdir('.'):
if not os.path.isfile(os.path.join('.', item)):
continue
if item.endswith('.pkl'):
continue
print 'Processing item ' + item
item_y = item.split('_')[0]
item_x = cv2.imread(item)
height, width = item_x.shape[:2]
# this was my requirement - skip it if you do not need it
if(height != 135 or width != 240):
continue
# get 3 channels
b,g,r = cv2.split(item_x)
item_x = [b,g,r]
item_x = np.array(item_x)
item_x = item_x.reshape(3,135*240)
if i_counter in test_index_array:
test_X.append(item_x)
test_y.append(item_y)
elif i_counter in validate_index_array:
val_X.append(item_x)
val_y.append(item_y)
else:
train_X.append(item_x)
train_y.append(item_y)
i_counter = i_counter + 1
# fix the dimensions. Flatten out the channel and intensity dimensions
train_X = np.array(train_X)
train_X = train_X.reshape(train_X.shape[0],train_X.shape[1]*train_X.shape[2])
test_X = np.array(test_X)
test_X = test_X.reshape(test_X.shape[0],test_X.shape[1]*test_X.shape[2])
val_X = np.array(val_X)
val_X = val_X.reshape(val_X.shape[0],val_X.shape[1]*val_X.shape[2])
train_y = np.array(train_y)
test_y = np.array(test_y)
val_y = np.array(val_y)
# shuffle the train and test arrays in unison
train_X,train_y = shuffle_in_unison(train_X,train_y)
test_X,test_y = shuffle_in_unison(test_X,test_y)
# pickle them
dataset['train'] = [train_X,train_y]
dataset['test'] = [test_X,test_y]
dataset['validate'] = [val_X,val_y]
output = open('pcount.pkl', 'wb')
cPickle.dump(dataset, output)
output.close`
一旦有了这种泡菜文件 您可以在这样convolutional_mlp.py使用它。
layer0_input = x.reshape((batch_size, 3, 135, 240))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (135-8+1 , 240-5+1) = (128, 236)
# maxpooling reduces this further to (128/2, 236/2) = (64, 118)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 64, 118)
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 3, 135, 240),
filter_shape=(nkerns[0], 3, 8, 5),
poolsize=(2, 2)
)
在logistic_sgd.py的load_data功能需要一个小的变化如下
f = open(dataset, 'rb')
dump = cPickle.load(f)
train_set = dump['train']
valid_set = dump['validate']
test_set = dump['test']
f.close()
希望这有助于
你提到顶部的形状是多通道图像的标准输入形状,例如彩色图像。你能否让你的问题更清楚? – eickenberg 2014-12-04 08:23:59
嗨,我的问题是非常具体的theano和此代码http://deeplearning.net/tutorial/code/convolutional_mlp.py。在此代码中,MNIST数字使用convnn和灰阶28 * 28输入图像进行分类。我正在为自己的彩色图像数据集创建对象检测的转换。我试图了解如何修改代码中的layer0输入数据结构,以允许theano将其理解为3个通道而不是1.希望可以清楚地看到 – Run2 2014-12-04 09:01:59